Tidy a(n) margins object
Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
## S3 method for class 'margins' tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
x |
A |
conf.int |
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
conf.level |
The confidence level to use for the confidence interval
if |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
The margins
package provides a way to obtain coefficient marginal
effects for a variety of (non-linear) models, such as logit or models with
multiway interaction terms. Note that the glance.margins()
method
requires rerunning the underlying model again, which can take some time.
Similarly, an augment.margins()
method is not currently supported, but
users can simply run the underlying model to obtain the same information.
A tibble::tibble()
with columns:
conf.high |
Upper bound on the confidence interval for the estimate. |
conf.low |
Lower bound on the confidence interval for the estimate. |
estimate |
The estimated value of the regression term. |
p.value |
The two-sided p-value associated with the observed statistic. |
statistic |
The value of a T-statistic to use in a hypothesis that the regression term is non-zero. |
std.error |
The standard error of the regression term. |
term |
The name of the regression term. |
if (requireNamespace("margins", quietly = TRUE)) { library(margins) ## Example 1: Logit model ## mod_log <- glm(am ~ cyl + hp + wt, data = mtcars, family = binomial) # Get tidied "naive" model coefficients tidy(mod_log) # Convert to marginal effects with margins::margins() marg_log <- margins(mod_log) # Get tidied marginal effects tidy(marg_log) tidy(marg_log, conf.int = TRUE) glance(marg_log) ## Requires running the underlying model again. Quick for this example. ## Not run: augment(marg_log) ## Not supported. augment(mod_log) ## But can get the same info by running on the underlying model. ## Example 2: Threeway interaction terms ## mod_ie <- lm(mpg ~ wt * cyl * disp, data = mtcars) # Get tidied "naive" model coefficients tidy(mod_ie) # Convert to marginal effects with margins::margins() marg_ie0 <- margins(mod_ie) # Get tidied marginal effects tidy(marg_ie0) glance(marg_ie0) # Marginal effects evaluated at specific values of a variable (here: cyl) marg_ie1 <- margins(mod_ie, at = list(cyl = c(4,6,8))) tidy(marg_ie1) # Marginal effects of one interaction variable (here: wt), modulated at # specific values of the two other interaction variables (here: cyl and drat) marg_ie2 <- margins(mod_ie, variables = "wt", ## Main var at = list(cyl = c(4,6,8), drat = c(3, 3.5, 4))) ## Modulating vars tidy(marg_ie2) }
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